Internal structure and conditions of permafrost mounds at Umiujaq in Nunavik, Canada, inferred from field investigation and electrical resistivity tomography
Bibliographic record
Abstract
A systematic approach was used for the interpretation of the electrical resistivity tomography carried out on two permafrost mounds at Umiujaq in Nunavik, Canada, to assess their internal structure and conditions. Prior information under the form of a geocryologic model of the permafrost mounds was integrated in the inversion of the pseudo-section of apparent electrical resistivity. The geocryologic model was developed from the synthesis of previous field investigations, including shallow and deep sampling, temperature and electrical resistivity logging, and cone penetration tests performed in the permafrost mounds. Values of electrical resistivity were ascribed to the different layers making of the geocryologic model to define a synthetic resistivity model of the permafrost mounds used as a reference model to constrain the inversion. The constrained resistivity model clearly show the presence of ice-rich cores in the permafrost mounds underscored by high resistivity values in excess of 30 000 Ωm, while the unfrozen zones surrounding the permafrost mounds are characterized by resistivity values lower than 1000 Ωm. The spatial distribution of unfrozen water and ice contents in the permafrost mounds were also assessed according to empirical relationships between the electrical resistivity and water contents. The ice content is highly variable and can be as high as 80% in the ice-rich cores, while the unfrozen water content varies between 2% and 5%. The integration of prior information in the inversion process leads to a more realistic constrained resistivity model showing sharp resistivity contrasts expected at the boundaries such as the permafrost table and base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".